Analytics Engineering

Robinhood’s Analytics Engineering team, part of the Data Science organization, is the backbone of our decision-making ecosystem. We design and deliver foundational data products that power everything from product innovation to regulatory compliance and operational excellence. Our mission is simple but ambitious: enable every team at Robinhood to access trustworthy, scalable, and self-serve analytics—so the right decisions happen faster.

We operate at the intersection of data engineering, data science, and product strategy, collaborating closely with product managers, engineers, and data scientists to transform raw data into clear, actionable intelligence.
As an Analytics Engineer, you will be a key architect of Robinhood’s data foundation. You’ll own the design and development of high-performance ETL pipelines, data models, and analytics tools that fuel critical decisions across the company. Your work will directly influence product strategy, regulatory reporting, and operational efficiency, ensuring Robinhood remains agile and data-driven at scale.

This is more than a build role—you’ll help define metrics, shape datasets, and set the standards for analytics excellence across the company. The systems and frameworks you create will have a long-lasting impact on Robinhood’s growth trajectory.

This role is based in our [Toronto, ON] office(s), with in-person attendance expected at least [3/4/5] days per week.

• Partner cross-functionally with product, engineering, and data science teams to scope and deliver high-impact analytics initiatives, from metric definitions to fully automated reporting solutions.
• Design and maintain reliable, scalable ETL pipelines and data models using modern data tools (e.g., Airflow, Spark), ensuring performance and accuracy at scale.
• Lead end-to-end development of analytics products—from ingestion to visualization—that meet mission-critical business, product, and regulatory needs.
  Build internal frameworks and tooling to make high-quality data more accessible and actionable across the organization.
• Collaborate with data scientists to transform raw data into meaningful insights that directly shape business and product outcomes.
  Champion analytics best practices and drive a culture of data literacy, empowering teams to confidently explore and interpret data on their own.

• 3+ years of experience in Analytics Engineering, Data Engineering, Data Science, or similar field.
• Strong expertise in advanced SQL, Python scripting, and Apache Spark (PySpark, Spark SQL) for data processing and transformation.
• Proficiency in building, maintaining, and optimizing ETL pipelines, using modern tools like Airflow or similar.
• Experience in building polished and performant dashboards using tools like Superset, Looker, Tableau.
• Strong familiarity with version control (GitHub), CI/CD, and modern development workflows.
• A strong product approach.
• Ability to work in a fast-paced, and highly cross-functional environment.

• Data Engineering experience
• Familiarity with HR systems like Greenhouse, Workday, and One Model
• Passion for working and learning in a fast-growing company
• Intense sense of curiosity
• Satisfaction from mentoring and encouraging others in your field

Our team is committed to providing an inclusive and welcoming interview experience for all candidates. If you require a specific accommodation during the application or interview process due to a physical or mental condition, please complete this Applicant Accommodation Form to notify our team. The form should only be completed if you need a specific accommodation.